Overview

Dataset statistics

Number of variables21
Number of observations41188
Missing cells12718
Missing cells (%)1.5%
Duplicate rows10
Duplicate rows (%)< 0.1%
Total size in memory29.9 MiB
Average record size in memory760.5 B

Variable types

Numeric10
Categorical7
Boolean4

Warnings

Dataset has 10 (< 0.1%) duplicate rowsDuplicates
previous_contacts is highly correlated with no_of_employeesHigh correlation
emp_var_rate is highly correlated with cons_price_idx and 2 other fieldsHigh correlation
cons_price_idx is highly correlated with emp_var_rate and 2 other fieldsHigh correlation
euribor3m is highly correlated with emp_var_rate and 2 other fieldsHigh correlation
no_of_employees is highly correlated with previous_contacts and 3 other fieldsHigh correlation
passed_days is highly correlated with previous_contactsHigh correlation
previous_contacts is highly correlated with passed_daysHigh correlation
emp_var_rate is highly correlated with cons_price_idx and 2 other fieldsHigh correlation
cons_price_idx is highly correlated with emp_var_rateHigh correlation
euribor3m is highly correlated with emp_var_rate and 1 other fieldsHigh correlation
no_of_employees is highly correlated with emp_var_rate and 1 other fieldsHigh correlation
age is highly correlated with passed_days and 1 other fieldsHigh correlation
duration is highly correlated with passed_days and 1 other fieldsHigh correlation
no_of_contacts is highly correlated with passed_days and 1 other fieldsHigh correlation
passed_days is highly correlated with age and 3 other fieldsHigh correlation
previous_contacts is highly correlated with age and 3 other fieldsHigh correlation
emp_var_rate is highly correlated with euribor3mHigh correlation
euribor3m is highly correlated with emp_var_rateHigh correlation
emp_var_rate is highly correlated with last_Cmonth and 6 other fieldsHigh correlation
y is highly correlated with cons_conf_idx and 2 other fieldsHigh correlation
last_Cmonth is highly correlated with emp_var_rate and 5 other fieldsHigh correlation
cons_conf_idx is highly correlated with emp_var_rate and 7 other fieldsHigh correlation
no_of_employees is highly correlated with emp_var_rate and 7 other fieldsHigh correlation
contact_type is highly correlated with emp_var_rate and 5 other fieldsHigh correlation
passed_days is highly correlated with previous_outcomeHigh correlation
job_type is highly correlated with education_level and 1 other fieldsHigh correlation
cons_price_idx is highly correlated with emp_var_rate and 6 other fieldsHigh correlation
euribor3m is highly correlated with emp_var_rate and 8 other fieldsHigh correlation
education_level is highly correlated with job_typeHigh correlation
previous_contacts is highly correlated with euribor3m and 1 other fieldsHigh correlation
age is highly correlated with job_typeHigh correlation
previous_outcome is highly correlated with emp_var_rate and 6 other fieldsHigh correlation
last_Cmonth is highly correlated with contact_typeHigh correlation
contact_type is highly correlated with last_CmonthHigh correlation
education_level has 1731 (4.2%) missing values Missing
default_credit has 8597 (20.9%) missing values Missing
housing_loan has 990 (2.4%) missing values Missing
personal_loan has 990 (2.4%) missing values Missing
passed_days has 39688 (96.4%) zeros Zeros
previous_contacts has 35563 (86.3%) zeros Zeros

Reproduction

Analysis started2021-08-14 11:29:12.792654
Analysis finished2021-08-14 11:31:09.087650
Duration1 minute and 56.29 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct78
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.02406041
Minimum17
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:09.241471image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile26
Q132
median38
Q347
95-th percentile58
Maximum98
Range81
Interquartile range (IQR)15

Descriptive statistics

Standard deviation10.42124998
Coefficient of variation (CV)0.2603746315
Kurtosis0.7913115312
Mean40.02406041
Median Absolute Deviation (MAD)7
Skewness0.7846968158
Sum1648511
Variance108.6024512
MonotonicityNot monotonic
2021-08-14T14:31:09.407534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
311947
 
4.7%
321846
 
4.5%
331833
 
4.5%
361780
 
4.3%
351759
 
4.3%
341745
 
4.2%
301714
 
4.2%
371475
 
3.6%
291453
 
3.5%
391432
 
3.5%
Other values (68)24204
58.8%
ValueCountFrequency (%)
175
 
< 0.1%
1828
 
0.1%
1942
 
0.1%
2065
 
0.2%
21102
 
0.2%
22137
 
0.3%
23226
 
0.5%
24463
1.1%
25598
1.5%
26698
1.7%
ValueCountFrequency (%)
982
 
< 0.1%
951
 
< 0.1%
941
 
< 0.1%
924
 
< 0.1%
912
 
< 0.1%
892
 
< 0.1%
8822
0.1%
871
 
< 0.1%
868
 
< 0.1%
8515
< 0.1%

job_type
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing330
Missing (%)0.8%
Memory size2.6 MiB
admin.
10422 
blue-collar
9254 
technician
6743 
services
3969 
management
2924 
Other values (6)
7546 

Length

Max length13
Median length10
Mean length8.971021587
Min length6

Characters and Unicode

Total characters366538
Distinct characters22
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousemaid
2nd rowservices
3rd rowservices
4th rowadmin.
5th rowservices

Common Values

ValueCountFrequency (%)
admin.10422
25.3%
blue-collar9254
22.5%
technician6743
16.4%
services3969
 
9.6%
management2924
 
7.1%
retired1720
 
4.2%
entrepreneur1456
 
3.5%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%

Length

2021-08-14T14:31:09.695268image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
admin10422
25.5%
blue-collar9254
22.6%
technician6743
16.5%
services3969
 
9.7%
management2924
 
7.2%
retired1720
 
4.2%
entrepreneur1456
 
3.6%
self-employed1421
 
3.5%
housemaid1060
 
2.6%
unemployed1014
 
2.5%

Most occurring characters

ValueCountFrequency (%)
e47273
12.9%
n34557
 
9.4%
a33327
 
9.1%
l31618
 
8.6%
i30657
 
8.4%
c26709
 
7.3%
r21031
 
5.7%
m19765
 
5.4%
d16512
 
4.5%
t14593
 
4.0%
Other values (12)90496
24.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter345441
94.2%
Dash Punctuation10675
 
2.9%
Other Punctuation10422
 
2.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e47273
13.7%
n34557
10.0%
a33327
9.6%
l31618
9.2%
i30657
8.9%
c26709
 
7.7%
r21031
 
6.1%
m19765
 
5.7%
d16512
 
4.8%
t14593
 
4.2%
Other values (10)69399
20.1%
Other Punctuation
ValueCountFrequency (%)
.10422
100.0%
Dash Punctuation
ValueCountFrequency (%)
-10675
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin345441
94.2%
Common21097
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e47273
13.7%
n34557
10.0%
a33327
9.6%
l31618
9.2%
i30657
8.9%
c26709
 
7.7%
r21031
 
6.1%
m19765
 
5.7%
d16512
 
4.8%
t14593
 
4.2%
Other values (10)69399
20.1%
Common
ValueCountFrequency (%)
-10675
50.6%
.10422
49.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII366538
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e47273
12.9%
n34557
 
9.4%
a33327
 
9.1%
l31618
 
8.6%
i30657
 
8.4%
c26709
 
7.3%
r21031
 
5.7%
m19765
 
5.4%
d16512
 
4.5%
t14593
 
4.0%
Other values (12)90496
24.7%

marital_status
Categorical

Distinct3
Distinct (%)< 0.1%
Missing80
Missing (%)0.2%
Memory size2.5 MiB
married
24928 
single
11568 
divorced
4612 

Length

Max length8
Median length7
Mean length6.830787195
Min length6

Characters and Unicode

Total characters280800
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmarried
2nd rowmarried
3rd rowmarried
4th rowmarried
5th rowmarried

Common Values

ValueCountFrequency (%)
married24928
60.5%
single11568
28.1%
divorced4612
 
11.2%
(Missing)80
 
0.2%

Length

2021-08-14T14:31:09.964514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T14:31:10.056174image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
married24928
60.6%
single11568
28.1%
divorced4612
 
11.2%

Most occurring characters

ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.2%
m24928
8.9%
a24928
8.9%
s11568
 
4.1%
n11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (3)13836
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter280800
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.2%
m24928
8.9%
a24928
8.9%
s11568
 
4.1%
n11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (3)13836
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
Latin280800
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.2%
m24928
8.9%
a24928
8.9%
s11568
 
4.1%
n11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (3)13836
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII280800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r54468
19.4%
i41108
14.6%
e41108
14.6%
d34152
12.2%
m24928
8.9%
a24928
8.9%
s11568
 
4.1%
n11568
 
4.1%
g11568
 
4.1%
l11568
 
4.1%
Other values (3)13836
 
4.9%

education_level
Categorical

HIGH CORRELATION
MISSING

Distinct7
Distinct (%)< 0.1%
Missing1731
Missing (%)4.2%
Memory size2.7 MiB
university.degree
12168 
high.school
9515 
basic.9y
6045 
professional.course
5243 
basic.4y
4176 
Other values (2)
2310 

Length

Max length19
Median length11
Mean length12.9615024
Min length8

Characters and Unicode

Total characters511422
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbasic.4y
2nd rowhigh.school
3rd rowhigh.school
4th rowbasic.6y
5th rowhigh.school

Common Values

ValueCountFrequency (%)
university.degree12168
29.5%
high.school9515
23.1%
basic.9y6045
14.7%
professional.course5243
12.7%
basic.4y4176
 
10.1%
basic.6y2292
 
5.6%
illiterate18
 
< 0.1%
(Missing)1731
 
4.2%

Length

2021-08-14T14:31:10.290677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T14:31:10.387239image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
university.degree12168
30.8%
high.school9515
24.1%
basic.9y6045
15.3%
professional.course5243
13.3%
basic.4y4176
 
10.6%
basic.6y2292
 
5.8%
illiterate18
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e59194
11.6%
i51643
 
10.1%
s49925
 
9.8%
.39439
 
7.7%
r34840
 
6.8%
o34759
 
6.8%
h28545
 
5.6%
c27271
 
5.3%
y24681
 
4.8%
g21683
 
4.2%
Other values (13)139442
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter459470
89.8%
Other Punctuation39439
 
7.7%
Decimal Number12513
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e59194
12.9%
i51643
11.2%
s49925
10.9%
r34840
 
7.6%
o34759
 
7.6%
h28545
 
6.2%
c27271
 
5.9%
y24681
 
5.4%
g21683
 
4.7%
a17774
 
3.9%
Other values (9)109155
23.8%
Decimal Number
ValueCountFrequency (%)
96045
48.3%
44176
33.4%
62292
 
18.3%
Other Punctuation
ValueCountFrequency (%)
.39439
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin459470
89.8%
Common51952
 
10.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e59194
12.9%
i51643
11.2%
s49925
10.9%
r34840
 
7.6%
o34759
 
7.6%
h28545
 
6.2%
c27271
 
5.9%
y24681
 
5.4%
g21683
 
4.7%
a17774
 
3.9%
Other values (9)109155
23.8%
Common
ValueCountFrequency (%)
.39439
75.9%
96045
 
11.6%
44176
 
8.0%
62292
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII511422
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e59194
11.6%
i51643
 
10.1%
s49925
 
9.8%
.39439
 
7.7%
r34840
 
6.8%
o34759
 
6.8%
h28545
 
5.6%
c27271
 
5.3%
y24681
 
4.8%
g21683
 
4.2%
Other values (13)139442
27.3%

default_credit
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing8597
Missing (%)20.9%
Memory size80.6 KiB
False
32588 
True
 
3
(Missing)
8597 
ValueCountFrequency (%)
False32588
79.1%
True3
 
< 0.1%
(Missing)8597
 
20.9%
2021-08-14T14:31:10.483175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

housing_loan
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing990
Missing (%)2.4%
Memory size80.6 KiB
True
21576 
False
18622 
(Missing)
 
990
ValueCountFrequency (%)
True21576
52.4%
False18622
45.2%
(Missing)990
 
2.4%
2021-08-14T14:31:10.526524image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

personal_loan
Boolean

MISSING

Distinct2
Distinct (%)< 0.1%
Missing990
Missing (%)2.4%
Memory size80.6 KiB
False
33950 
True
6248 
(Missing)
 
990
ValueCountFrequency (%)
False33950
82.4%
True6248
 
15.2%
(Missing)990
 
2.4%
2021-08-14T14:31:10.570282image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

contact_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
cellular
26144 
telephone
15044 

Length

Max length9
Median length8
Mean length8.365252015
Min length8

Characters and Unicode

Total characters344548
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtelephone
2nd rowtelephone
3rd rowtelephone
4th rowtelephone
5th rowtelephone

Common Values

ValueCountFrequency (%)
cellular26144
63.5%
telephone15044
36.5%

Length

2021-08-14T14:31:10.772028image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T14:31:10.851883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
cellular26144
63.5%
telephone15044
36.5%

Most occurring characters

ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter344548
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Latin344548
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII344548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l93476
27.1%
e71276
20.7%
c26144
 
7.6%
u26144
 
7.6%
a26144
 
7.6%
r26144
 
7.6%
t15044
 
4.4%
p15044
 
4.4%
h15044
 
4.4%
o15044
 
4.4%

last_Cmonth
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
may
13769 
jul
7174 
aug
6178 
jun
5318 
nov
4101 
Other values (5)
4648 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmay
2nd rowmay
3rd rowmay
4th rowmay
5th rowmay

Common Values

ValueCountFrequency (%)
may13769
33.4%
jul7174
17.4%
aug6178
15.0%
jun5318
 
12.9%
nov4101
 
10.0%
apr2632
 
6.4%
oct718
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Length

2021-08-14T14:31:11.081933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T14:31:11.176390image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
may13769
33.4%
jul7174
17.4%
aug6178
15.0%
jun5318
 
12.9%
nov4101
 
10.0%
apr2632
 
6.4%
oct718
 
1.7%
sep570
 
1.4%
mar546
 
1.3%
dec182
 
0.4%

Most occurring characters

ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123564
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a23125
18.7%
u18670
15.1%
m14315
11.6%
y13769
11.1%
j12492
10.1%
n9419
7.6%
l7174
 
5.8%
g6178
 
5.0%
o4819
 
3.9%
v4101
 
3.3%
Other values (7)9502
7.7%

last_Cday
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
thu
8623 
mon
8514 
wed
8134 
tue
8090 
fri
7827 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters123564
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmon
2nd rowmon
3rd rowmon
4th rowmon
5th rowmon

Common Values

ValueCountFrequency (%)
thu8623
20.9%
mon8514
20.7%
wed8134
19.7%
tue8090
19.6%
fri7827
19.0%

Length

2021-08-14T14:31:11.474252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T14:31:11.560842image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
thu8623
20.9%
mon8514
20.7%
wed8134
19.7%
tue8090
19.6%
fri7827
19.0%

Most occurring characters

ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter123564
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

Most occurring scripts

ValueCountFrequency (%)
Latin123564
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII123564
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t16713
13.5%
u16713
13.5%
e16224
13.1%
h8623
7.0%
m8514
6.9%
o8514
6.9%
n8514
6.9%
w8134
6.6%
d8134
6.6%
f7827
6.3%
Other values (2)15654
12.7%

duration
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1544
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean258.2850102
Minimum0
Maximum4918
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:11.681154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile36
Q1102
median180
Q3319
95-th percentile752.65
Maximum4918
Range4918
Interquartile range (IQR)217

Descriptive statistics

Standard deviation259.2792488
Coefficient of variation (CV)1.003849386
Kurtosis20.24793801
Mean258.2850102
Median Absolute Deviation (MAD)94
Skewness3.263141255
Sum10638243
Variance67225.72888
MonotonicityNot monotonic
2021-08-14T14:31:11.841646image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90170
 
0.4%
85170
 
0.4%
136168
 
0.4%
73167
 
0.4%
124164
 
0.4%
87162
 
0.4%
72161
 
0.4%
104161
 
0.4%
111160
 
0.4%
106159
 
0.4%
Other values (1534)39546
96.0%
ValueCountFrequency (%)
04
 
< 0.1%
13
 
< 0.1%
21
 
< 0.1%
33
 
< 0.1%
412
 
< 0.1%
530
 
0.1%
637
0.1%
754
0.1%
869
0.2%
977
0.2%
ValueCountFrequency (%)
49181
< 0.1%
41991
< 0.1%
37851
< 0.1%
36431
< 0.1%
36311
< 0.1%
35091
< 0.1%
34221
< 0.1%
33661
< 0.1%
33221
< 0.1%
32841
< 0.1%

no_of_contacts
Real number (ℝ≥0)

HIGH CORRELATION

Distinct42
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.567592503
Minimum1
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:11.992147image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum56
Range55
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.770013543
Coefficient of variation (CV)1.078836903
Kurtosis36.97979514
Mean2.567592503
Median Absolute Deviation (MAD)1
Skewness4.762506697
Sum105754
Variance7.672975028
MonotonicityNot monotonic
2021-08-14T14:31:12.127883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
Other values (32)869
 
2.1%
ValueCountFrequency (%)
117642
42.8%
210570
25.7%
35341
 
13.0%
42651
 
6.4%
51599
 
3.9%
6979
 
2.4%
7629
 
1.5%
8400
 
1.0%
9283
 
0.7%
10225
 
0.5%
ValueCountFrequency (%)
561
 
< 0.1%
432
 
< 0.1%
422
 
< 0.1%
411
 
< 0.1%
402
 
< 0.1%
391
 
< 0.1%
371
 
< 0.1%
355
< 0.1%
343
< 0.1%
334
< 0.1%

passed_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2212294843
Minimum0
Maximum27
Zeros39688
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:12.261292image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.348874125
Coefficient of variation (CV)6.097171585
Kurtosis76.40203714
Mean0.2212294843
Median Absolute Deviation (MAD)0
Skewness7.939537186
Sum9112
Variance1.819461406
MonotonicityNot monotonic
2021-08-14T14:31:12.397184image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
039688
96.4%
3439
 
1.1%
6412
 
1.0%
4118
 
0.3%
964
 
0.2%
261
 
0.1%
760
 
0.1%
1258
 
0.1%
1052
 
0.1%
546
 
0.1%
Other values (16)190
 
0.5%
ValueCountFrequency (%)
039688
96.4%
126
 
0.1%
261
 
0.1%
3439
 
1.1%
4118
 
0.3%
546
 
0.1%
6412
 
1.0%
760
 
0.1%
818
 
< 0.1%
964
 
0.2%
ValueCountFrequency (%)
271
 
< 0.1%
261
 
< 0.1%
251
 
< 0.1%
223
 
< 0.1%
212
 
< 0.1%
201
 
< 0.1%
193
 
< 0.1%
187
< 0.1%
178
< 0.1%
1611
< 0.1%

previous_contacts
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1729629989
Minimum0
Maximum7
Zeros35563
Zeros (%)86.3%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:12.734358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4949010798
Coefficient of variation (CV)2.861311858
Kurtosis20.10881622
Mean0.1729629989
Median Absolute Deviation (MAD)0
Skewness3.832042243
Sum7124
Variance0.2449270788
MonotonicityNot monotonic
2021-08-14T14:31:12.840050image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
035563
86.3%
14561
 
11.1%
2754
 
1.8%
3216
 
0.5%
470
 
0.2%
518
 
< 0.1%
65
 
< 0.1%
71
 
< 0.1%
ValueCountFrequency (%)
71
 
< 0.1%
65
 
< 0.1%
518
 
< 0.1%
470
 
0.2%
3216
 
0.5%
2754
 
1.8%
14561
 
11.1%
035563
86.3%

previous_outcome
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
nonexistent
35563 
failure
4252 
success
 
1373

Length

Max length11
Median length11
Mean length10.45372439
Min length7

Characters and Unicode

Total characters430568
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownonexistent
2nd rownonexistent
3rd rownonexistent
4th rownonexistent
5th rownonexistent

Common Values

ValueCountFrequency (%)
nonexistent35563
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Length

2021-08-14T14:31:13.118034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-14T14:31:13.215482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
nonexistent35563
86.3%
failure4252
 
10.3%
success1373
 
3.3%

Most occurring characters

ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter430568
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin430568
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII430568
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n106689
24.8%
e76751
17.8%
t71126
16.5%
i39815
 
9.2%
s39682
 
9.2%
o35563
 
8.3%
x35563
 
8.3%
u5625
 
1.3%
f4252
 
1.0%
a4252
 
1.0%
Other values (3)11250
 
2.6%

emp_var_rate
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08188550063
Minimum-3.4
Maximum1.4
Zeros0
Zeros (%)0.0%
Negative17191
Negative (%)41.7%
Memory size321.9 KiB
2021-08-14T14:31:13.291276image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-3.4
5-th percentile-2.9
Q1-1.8
median1.1
Q31.4
95-th percentile1.4
Maximum1.4
Range4.8
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation1.570959741
Coefficient of variation (CV)19.18483405
Kurtosis-1.062631525
Mean0.08188550063
Median Absolute Deviation (MAD)0.3
Skewness-0.7240955492
Sum3372.7
Variance2.467914506
MonotonicityNot monotonic
2021-08-14T14:31:13.397403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1.416234
39.4%
-1.89184
22.3%
1.17763
18.8%
-0.13683
 
8.9%
-2.91663
 
4.0%
-3.41071
 
2.6%
-1.7773
 
1.9%
-1.1635
 
1.5%
-3172
 
0.4%
-0.210
 
< 0.1%
ValueCountFrequency (%)
-3.41071
 
2.6%
-3172
 
0.4%
-2.91663
 
4.0%
-1.89184
22.3%
-1.7773
 
1.9%
-1.1635
 
1.5%
-0.210
 
< 0.1%
-0.13683
 
8.9%
1.17763
18.8%
1.416234
39.4%
ValueCountFrequency (%)
1.416234
39.4%
1.17763
18.8%
-0.13683
 
8.9%
-0.210
 
< 0.1%
-1.1635
 
1.5%
-1.7773
 
1.9%
-1.89184
22.3%
-2.91663
 
4.0%
-3172
 
0.4%
-3.41071
 
2.6%

cons_price_idx
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.57566437
Minimum92.201
Maximum94.767
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:13.508750image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum92.201
5-th percentile92.713
Q193.075
median93.749
Q393.994
95-th percentile94.465
Maximum94.767
Range2.566
Interquartile range (IQR)0.919

Descriptive statistics

Standard deviation0.578840049
Coefficient of variation (CV)0.00618579684
Kurtosis-0.8298085772
Mean93.57566437
Median Absolute Deviation (MAD)0.38
Skewness-0.2308876514
Sum3854194.464
Variance0.3350558023
MonotonicityNot monotonic
2021-08-14T14:31:13.643851image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
93.9947763
18.8%
93.9186685
16.2%
92.8935794
14.1%
93.4445175
12.6%
94.4654374
10.6%
93.23616
8.8%
93.0752458
 
6.0%
92.201770
 
1.9%
92.963715
 
1.7%
92.431447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
92.201770
 
1.9%
92.379267
 
0.6%
92.431447
 
1.1%
92.469178
 
0.4%
92.649357
 
0.9%
92.713172
 
0.4%
92.75610
 
< 0.1%
92.843282
 
0.7%
92.8935794
14.1%
92.963715
 
1.7%
ValueCountFrequency (%)
94.767128
 
0.3%
94.601204
 
0.5%
94.4654374
10.6%
94.215311
 
0.8%
94.199303
 
0.7%
94.055229
 
0.6%
94.027233
 
0.6%
93.9947763
18.8%
93.9186685
16.2%
93.876212
 
0.5%

cons_conf_idx
Real number (ℝ)

HIGH CORRELATION

Distinct26
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-40.50260027
Minimum-50.8
Maximum-26.9
Zeros0
Zeros (%)0.0%
Negative41188
Negative (%)100.0%
Memory size321.9 KiB
2021-08-14T14:31:13.774512image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-50.8
5-th percentile-47.1
Q1-42.7
median-41.8
Q3-36.4
95-th percentile-33.6
Maximum-26.9
Range23.9
Interquartile range (IQR)6.3

Descriptive statistics

Standard deviation4.628197856
Coefficient of variation (CV)-0.1142691537
Kurtosis-0.3585583105
Mean-40.50260027
Median Absolute Deviation (MAD)4.4
Skewness0.3031798587
Sum-1668221.1
Variance21.4202154
MonotonicityNot monotonic
2021-08-14T14:31:13.905534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
-36.47763
18.8%
-42.76685
16.2%
-46.25794
14.1%
-36.15175
12.6%
-41.84374
10.6%
-423616
8.8%
-47.12458
 
6.0%
-31.4770
 
1.9%
-40.8715
 
1.7%
-26.9447
 
1.1%
Other values (16)3391
8.2%
ValueCountFrequency (%)
-50.8128
 
0.3%
-50282
 
0.7%
-49.5204
 
0.5%
-47.12458
 
6.0%
-46.25794
14.1%
-45.910
 
< 0.1%
-42.76685
16.2%
-423616
8.8%
-41.84374
10.6%
-40.8715
 
1.7%
ValueCountFrequency (%)
-26.9447
 
1.1%
-29.8267
 
0.6%
-30.1357
 
0.9%
-31.4770
 
1.9%
-33172
 
0.4%
-33.6178
 
0.4%
-34.6174
 
0.4%
-34.8264
 
0.6%
-36.15175
12.6%
-36.47763
18.8%

euribor3m
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct316
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.621290813
Minimum0.634
Maximum5.045
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:14.052905image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0.634
5-th percentile0.797
Q11.344
median4.857
Q34.961
95-th percentile4.966
Maximum5.045
Range4.411
Interquartile range (IQR)3.617

Descriptive statistics

Standard deviation1.734447405
Coefficient of variation (CV)0.4789583313
Kurtosis-1.406802622
Mean3.621290813
Median Absolute Deviation (MAD)0.108
Skewness-0.7091879564
Sum149153.726
Variance3.0083078
MonotonicityNot monotonic
2021-08-14T14:31:14.206628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.8572868
 
7.0%
4.9622613
 
6.3%
4.9632487
 
6.0%
4.9611902
 
4.6%
4.8561210
 
2.9%
4.9641175
 
2.9%
1.4051169
 
2.8%
4.9651071
 
2.6%
4.8641044
 
2.5%
4.961013
 
2.5%
Other values (306)24636
59.8%
ValueCountFrequency (%)
0.6348
 
< 0.1%
0.63543
0.1%
0.63614
 
< 0.1%
0.6376
 
< 0.1%
0.6387
 
< 0.1%
0.63916
 
< 0.1%
0.6410
 
< 0.1%
0.64235
0.1%
0.64323
0.1%
0.64438
0.1%
ValueCountFrequency (%)
5.0459
 
< 0.1%
57
 
< 0.1%
4.97172
 
0.4%
4.968992
 
2.4%
4.967643
 
1.6%
4.966622
 
1.5%
4.9651071
2.6%
4.9641175
2.9%
4.9632487
6.0%
4.9622613
6.3%

no_of_employees
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5167.035911
Minimum4963.6
Maximum5228.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.9 KiB
2021-08-14T14:31:14.335933image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum4963.6
5-th percentile5017.5
Q15099.1
median5191
Q35228.1
95-th percentile5228.1
Maximum5228.1
Range264.5
Interquartile range (IQR)129

Descriptive statistics

Standard deviation72.25152767
Coefficient of variation (CV)0.01398316732
Kurtosis-0.003760375696
Mean5167.035911
Median Absolute Deviation (MAD)37.1
Skewness-1.044262407
Sum212819875.1
Variance5220.28325
MonotonicityNot monotonic
2021-08-14T14:31:14.448659image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
5228.116234
39.4%
5099.18534
20.7%
51917763
18.8%
5195.83683
 
8.9%
5076.21663
 
4.0%
5017.51071
 
2.6%
4991.6773
 
1.9%
5008.7650
 
1.6%
4963.6635
 
1.5%
5023.5172
 
0.4%
ValueCountFrequency (%)
4963.6635
 
1.5%
4991.6773
 
1.9%
5008.7650
 
1.6%
5017.51071
 
2.6%
5023.5172
 
0.4%
5076.21663
 
4.0%
5099.18534
20.7%
5176.310
 
< 0.1%
51917763
18.8%
5195.83683
8.9%
ValueCountFrequency (%)
5228.116234
39.4%
5195.83683
 
8.9%
51917763
18.8%
5176.310
 
< 0.1%
5099.18534
20.7%
5076.21663
 
4.0%
5023.5172
 
0.4%
5017.51071
 
2.6%
5008.7650
 
1.6%
4991.6773
 
1.9%

y
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size40.3 KiB
False
36548 
True
4640 
ValueCountFrequency (%)
False36548
88.7%
True4640
 
11.3%
2021-08-14T14:31:14.535395image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Interactions

2021-08-14T14:30:53.744544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:53.903369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.046687image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.190910image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.332864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.481417image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.624125image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.767068image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:54.910438image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.049335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.191156image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.329641image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.458931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.587847image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.716835image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.854253image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:55.980982image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.109342image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.236637image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.368796image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.498580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.637800image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.767525image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:56.896831image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.128450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.264870image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.392326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.522038image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.649755image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.776336image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:57.904483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.042662image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.172916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.301574image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.429514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.565890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.691922image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.820265image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:58.947238image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.072556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.200915image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.349918image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.490626image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.628748image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.769425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:30:59.913947image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.051192image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.189369image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.326402image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.460924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.598769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.734580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.860754image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:00.986188image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.111240image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.242261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.363425image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.618832image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.741543image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.863544image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:01.992909image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.131353image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.259824image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.387288image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.514932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.648378image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.771871image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:02.896964image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.021560image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.144830image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.271051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.406867image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.534407image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.661022image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.787361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:03.919242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.041864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.166366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.289522image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.411545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.580486image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.716778image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.843366image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:04.969249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.094120image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.225306image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.346340image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.469523image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.591335image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.711814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.835932image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:05.972394image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.100099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.227514image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.354291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.486558image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.609609image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.734483image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:06.858226image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-08-14T14:31:07.143450image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-08-14T14:31:14.635167image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-14T14:31:14.836191image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-14T14:31:15.036080image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-14T14:31:15.258124image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-14T14:31:15.535707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-14T14:31:07.445052image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-14T14:31:08.158904image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-14T14:31:08.567809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-14T14:31:08.794031image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

agejob_typemarital_statuseducation_leveldefault_credithousing_loanpersonal_loancontact_typelast_Cmonthlast_Cdaydurationno_of_contactspassed_daysprevious_contactsprevious_outcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mno_of_employeesy
056housemaidmarriedbasic.4ynononotelephonemaymon261100nonexistent1.193.994-36.44.8575191.0no
157servicesmarriedhigh.schoolNaNnonotelephonemaymon149100nonexistent1.193.994-36.44.8575191.0no
237servicesmarriedhigh.schoolnoyesnotelephonemaymon226100nonexistent1.193.994-36.44.8575191.0no
340admin.marriedbasic.6ynononotelephonemaymon151100nonexistent1.193.994-36.44.8575191.0no
456servicesmarriedhigh.schoolnonoyestelephonemaymon307100nonexistent1.193.994-36.44.8575191.0no
545servicesmarriedbasic.9yNaNnonotelephonemaymon198100nonexistent1.193.994-36.44.8575191.0no
659admin.marriedprofessional.coursenononotelephonemaymon139100nonexistent1.193.994-36.44.8575191.0no
741blue-collarmarriedNaNNaNnonotelephonemaymon217100nonexistent1.193.994-36.44.8575191.0no
824techniciansingleprofessional.coursenoyesnotelephonemaymon380100nonexistent1.193.994-36.44.8575191.0no
925servicessinglehigh.schoolnoyesnotelephonemaymon50100nonexistent1.193.994-36.44.8575191.0no

Last rows

agejob_typemarital_statuseducation_leveldefault_credithousing_loanpersonal_loancontact_typelast_Cmonthlast_Cdaydurationno_of_contactspassed_daysprevious_contactsprevious_outcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mno_of_employeesy
4117862retiredmarrieduniversity.degreenononocellularnovthu483263success-1.194.767-50.81.0314963.6yes
4117964retireddivorcedprofessional.coursenoyesnocellularnovfri151300nonexistent-1.194.767-50.81.0284963.6no
4118036admin.marrieduniversity.degreenononocellularnovfri254200nonexistent-1.194.767-50.81.0284963.6no
4118137admin.marrieduniversity.degreenoyesnocellularnovfri281100nonexistent-1.194.767-50.81.0284963.6yes
4118229unemployedsinglebasic.4ynoyesnocellularnovfri112191success-1.194.767-50.81.0284963.6no
4118373retiredmarriedprofessional.coursenoyesnocellularnovfri334100nonexistent-1.194.767-50.81.0284963.6yes
4118446blue-collarmarriedprofessional.coursenononocellularnovfri383100nonexistent-1.194.767-50.81.0284963.6no
4118556retiredmarrieduniversity.degreenoyesnocellularnovfri189200nonexistent-1.194.767-50.81.0284963.6no
4118644technicianmarriedprofessional.coursenononocellularnovfri442100nonexistent-1.194.767-50.81.0284963.6yes
4118774retiredmarriedprofessional.coursenoyesnocellularnovfri239301failure-1.194.767-50.81.0284963.6no

Duplicate rows

Most frequently occurring

agejob_typemarital_statuseducation_leveldefault_credithousing_loanpersonal_loancontact_typelast_Cmonthlast_Cdaydurationno_of_contactspassed_daysprevious_contactsprevious_outcomeemp_var_ratecons_price_idxcons_conf_idxeuribor3mno_of_employeesy# duplicates
024servicessinglehigh.schoolnoyesnocellularaprtue114100nonexistent-1.893.075-47.11.4235099.1no2
127techniciansingleprofessional.coursenononocellularjulmon331200nonexistent1.493.918-42.74.9625228.1no2
232techniciansingleprofessional.coursenoyesnocellularjulthu128100nonexistent1.493.918-42.74.9685228.1no2
335admin.marrieduniversity.degreenoyesnocellularmayfri348400nonexistent-1.892.893-46.21.3135099.1no2
439admin.marrieduniversity.degreenononocellularnovtue123200nonexistent-0.193.200-42.04.1535195.8no2
539blue-collarmarriedbasic.6ynononotelephonemaythu124100nonexistent1.193.994-36.44.8555191.0no2
641technicianmarriedprofessional.coursenoyesnocellularaugtue127100nonexistent1.493.444-36.14.9665228.1no2
745admin.marrieduniversity.degreenononocellularjulthu252100nonexistent-2.992.469-33.61.0725076.2yes2
847techniciandivorcedhigh.schoolnoyesnocellularjulthu43300nonexistent1.493.918-42.74.9625228.1no2
971retiredsingleuniversity.degreenononotelephoneocttue120100nonexistent-3.492.431-26.90.7425017.5no2